14 citations · 18 across the 14 of their papers we have counts for
14 papers
Understanding Task Transfer in Vision-Language Models
Bhuvan Sachdeva, Karan Uppal, Abhinav Java +1
Vision-Language Models (VLMs) perform well on multimodal benchmarks but lag behind humans and specialized models on visual perception tasks like depth estimation or object counting…
Characterizing Deep Research: A Benchmark and Formal Definition
Abhinav Java, Ashmit Khandelwal, Sukruta Midigeshi +6
Information tasks such as writing surveys or analytical reports require complex search and reasoning, and have recently been grouped under the umbrella of \textit{deep research} --…
FrugalRAG: Less is More in RL Finetuning for Multi-Hop Question Answering
Abhinav Java, Srivathsan Koundinyan, Nagarajan Natarajan +1
Reinforcement learning (RL) based on the final answer's reward has driven recent progress in small language models (SLMs) on reasoning-heavy tasks such as math and code. However, a…
Towards Efficient Exemplar Based Image Editing with Multimodal VLMs
Avadhoot Jadhav, Ashutosh Srivastava, Abhinav Java +4
Text-to-Image Diffusion models have enabled a wide array of image editing applications. However, capturing all types of edits through text alone can be challenging and cumbersome.…
Towards Operationalizing Right to Data Protection
Abhinav Java, Simra Shahid, Chirag Agarwal
The widespread practice of indiscriminate data scraping to fine-tune language models (LMs) raises significant legal and ethical concerns, particularly regarding compliance with dat…
ReEdit: Multimodal Exemplar-Based Image Editing with Diffusion Models
Ashutosh Srivastava, Tarun Ram Menta, Abhinav Java +4
Modern Text-to-Image (T2I) Diffusion models have revolutionized image editing by enabling the generation of high-quality photorealistic images. While the de facto method for perfor…
Thinking Fair and Slow: On the Efficacy of Structured Prompts for Debiasing Language Models
Shaz Furniturewala, Surgan Jandial, Abhinav Java +4
Existing debiasing techniques are typically training-based or require access to the model's internals and output distributions, so they are inaccessible to end-users looking to ada…
LEAST: "Local" text-conditioned image style transfer
Silky Singh, Surgan Jandial, Simra Shahid +1
Text-conditioned style transfer enables users to communicate their desired artistic styles through text descriptions, offering a new and expressive means of achieving stylization.…
All Should Be Equal in the Eyes of Language Models: Counterfactually Aware Fair Text Generation
Pragyan Banerjee, Abhinav Java, Surgan Jandial +4
Fairness in Language Models (LMs) remains a longstanding challenge, given the inherent biases in training data that can be perpetuated by models and affect the downstream tasks. Re…
One-Shot Doc Snippet Detection: Powering Search in Document Beyond Text
Abhinav Java, Shripad Deshmukh, Milan Aggarwal +3
Active consumption of digital documents has yielded scope for research in various applications, including search. Traditionally, searching within a document has been cast as a text…
Learning to Censor by Noisy Sampling
Ayush Chopra, Abhinav Java, Abhishek Singh +2
Point clouds are an increasingly ubiquitous input modality and the raw signal can be efficiently processed with recent progress in deep learning. This signal may, often inadvertent…
AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learning
Ayush Chopra, Surya Kant Sahu, Abhishek Singh +4
Distributed deep learning frameworks like federated learning (FL) and its variants are enabling personalized experiences across a wide range of web clients and mobile/IoT devices.…
Introducing Self-Attention to Target Attentive Graph Neural Networks
Sai Mitheran, Abhinav Java, Surya Kant Sahu +1
Session-based recommendation systems suggest relevant items to users by modeling user behavior and preferences using short-term anonymous sessions. Existing methods leverage Graph…
Rethinking Neural Networks With Benford's Law
Surya Kant Sahu, Abhinav Java, Arshad Shaikh +1
Benford's Law (BL) or the Significant Digit Law defines the probability distribution of the first digit of numerical values in a data sample. This Law is observed in many naturally…